data-ai MCP Server
Self-hosted AI assistant with voice satellites, 100+ tool integrations, RAG knowledge base, and full offline capability. Built with FastAPI + React + Ollama.
Discovered via github-topic:mcp and last synced 2mo ago.
Install instructions not detected yet
Check the source repository for the latest setup steps.
SearXNG metasearch
No
OpenWeatherMap forecasts
Workflow automation
NewsAPI headlines
Media server queries
Media renderer control with gapless queue
Model
Trigger
Exchange, Google, CalDAV (multi-account)
No
### Knowledge Base (RAG) - **Hybrid search** — dense embeddings (pgvector) + BM25 full-text (German FTS, OR matching), combined via RRF - **Formats** — PDF, DOCX, PPTX, XLSX, HTML, Markdown, TXT (with EasyOCR fallback for garbled PDFs) - **Knowledge bases** — thematic collections with sharing and access control - **Agent tool** — `knowledge_search` for combined RAG + Paperless search within the agent loop - **Knowledge Graph** — entity-relation triples with post-extraction validation, string-similarity dedup, bulk cleanup API, admin dashboard ### Multi-Room Voice Satellites
Content
- **Privacy-aware TTS** — notifications respect room occupancy (public / personal / confidential) - **Automation hooks** — `enter_room`, `leave_room`, `first_arrived`, `last_left` events fire webhooks for n8n / Home Assistant - **Presence dashboard** — real-time room occupancy in the admin UI ### Security & Access Control - Role-permission based access control (RPBAC) with JWT - Rate limiting, circuit breakers, trusted proxy support - Docker secrets management for production ### Platform - Progressive Web App (desktop, tablet, mobile) - Dark mode (light, dark, system) - Multilingual (German, English) - Prometheus metrics (opt-in) ## Quick Start ```bash git clone https://github.com/ebongard/renfield.git && cd renfield cp .env.example .env # configure your settings docker compose up -d # start the stack docker exec -it renfield-ollama ollama pull qwen3:8b # download LLM ``` Open **http://localhost:3000** and start chatting. > For GPU acceleration, use `docker compose -f docker-compose.prod.yml up -d` with NVIDIA Container Toolkit. ## Architecture <p align="center"><img src="docs/assets/architecture.png" width="900" alt="Renfield Architecture"></p> ## Configuration All settings via `.env`, loaded by Pydantic Settings. See [docs/ENVIRONMENT_VARIABLES.md](docs/ENVIRONMENT_VARIABLES.md) for the full reference. ### LLM (Multi-Model) Separate models for different tasks: ```env OLLAMA_URL=http://ollama:11434 OLLAMA_CHAT_MODEL=qwen3:14b # chat responses OLLAMA_INTENT_MODEL=qwen3:8b # intent recognition OLLAMA_RAG_MODEL=qwen3:14b # RAG answers OLLAMA_EMBED_MODEL=nomic-embed-text # embeddings (768 dim) ``` **Pluggable inference — not everything runs on Ollama.** The LLM tier speaks an OpenAI-compatible protocol, so each task can route to a different backend (`LLM_OPENAI_FOR_*`). Ollama is the simple default; the reference deployment splits inference across dedicated GPU servers:
**llama.cpp**
**Ollama**
Use Case